Rengers, Timothy A.

ImportanceThis review aims to assess the benefits and risks of implementing large language model (LLM) solutions in an academic surgical setting.ObservationsThe integration of LLMs and artificial intelligence (AI) into surgical practice has generated international attention with the emergence of OpenAI’s ChatGPT and Google’s Bard. From an administrative standpoint, LLMs have the potential to revolutionize academic practices by reducing administrative burdens and improving efficiency. LLMs have the potential to facilitate surgical research by increasing writing efficiency, building predictive models, and aiding in large dataset analysis. From a clinical standpoint, LLMs can enhance efficiency by triaging patient concerns and generating automated responses. However, challenges exist, such as the need for improved LLM generalization performance, validating content, and addressing ethical concerns. In addition, patient privacy, potential bias in training, and legal responsibility are important considerations that require attention. Research and precautionary measures are necessary to ensure safe and unbiased use of LLMs in surgery.Conclusions and RelevanceAlthough limitations exist, LLMs hold promise for enhancing surgical efficiency while still prioritizing patient care. The authors recommend that the academic surgical community further investigate the potential applications of LLMs while being cautious about potential harms.

Diversity is a catalyst for progress that prevents institutional stagnation and, by extension, averts descent to mediocrity. This review focuses on the available data concerning hepatopancreatobiliary (HPB) surgical workforce demographics and identifies evidence-based strategies that may enhance justice, equity, diversity, and inclusion for HPB surgeons and their patients. We report that the current United States HPB surgical workforce does not reflect the population it serves. We review data describing disparity-perpetuating hurdles confronting physicians from minority groups underrepresented in medicine at each stage of training. We further examine evidence showing widespread racial and socioeconomic disparities in HPB surgical care and review the effects of workforce diversity and physician–patient demographic concordance on healthcare outcomes. Evidence-based mitigators of structural racism and segregation are reviewed, including tailored interventions that can address social determinants of health toward the achievement of true excellence in HPB surgical care. Lastly, select evidence-based data driving surgical workforce solutions are reviewed, including intentional compensation plans, mentorship, and sponsorship.